// MIT TECH REVIEW — INTELLIGENZA ARTIFICIALE
Pairing industrial AI autonomy and human control
Living digital twins are helping industrial plants run more like living systems, and setting the stage for industries that work in sync.
Most people associate industrial environments with cold steel and clockwork machinery. Functionally, though, a plant or factory is more like a living thing: It has a pulse, even a metabolism. It goes through wear and tear as it ages, and signs of deteriorating health often show up as barely perceptible irregularities.
Digital twin technology has long aimed to mirror that dynamism and model anomalies that may be symptomatic of larger problems. But earlier implementations were a few beats behind physical reality; they were incapable of real-time analysis and unable to proactively address any issues detected. They focused more on simulation, observation and what-if scenario planning, rather than closed-loop execution. Rather than serving as a digital mirror of reality that can spot anomalies or suggest optimizations, a living digital twin combines a dynamic, high-fidelity digital representation of complex operations with the capacity to intervene in the physical world through operations and control systems.
AI is at the center of this change. Rob McGreevy, chief product officer at AVEVA, likens newer, AI-infused digital twins to organisms that grow and evolve. These “living” versions are adaptive, evergreen models of a plant’s assets and processes. Because they keep pace with live conditions and connect systems that once ran in isolation, they provide a previously missing layer of visibility and orchestration that better reflects the organic rhythms of the facility itself.
Now, agentic AI is also enabling industrial systems to move beyond diagnostics or suggestions and act of their own accord. As the technology progresses toward greater autonomy, it’s starting to develop the reflexes for a kind of industrial homeostasis, in which small, constant adjustments keep a plant or factory healthy and humming.
All that said, human judgment and operator oversight still play an instrumental role by defining what healthy looks like to begin with—and flagging when AI systems are falling out of alignment. Living digital twins provide the means for human engineers to monitor what AI is doing and ensure execution is on track.
Living digital twins link engineering and design information with real-time operational data through a digital thread, or a connected record of an asset that follows it through its lifecycle. A couple of notable technological shifts have helped string this thread together: Sensors (which have gotten more affordable in recent years) now stream telemetry from IoT devices and industrial control equipment, and software platforms bring that information into user-friendly interfaces. Predictive and physics-based models can also provide AI-enriched insights into how a machine or process might behave.
The lenses through which operators view digital twins have evolved in turn. “You can look at the digital twin through the lens of sustainability: What are my emissions?” says McGreevy. “Or, through the lens of maintenance: Is this asset performing in a healthy way? Or, through a performance metric: Is it producing the number of widgets I need?”
The next phase is letting AI act on select insights those lenses illuminate. For their part, manufacturers are eager for this chapter: In a 2026 Manufacturing Leadership Council survey of manufacturers (opens in new tab), 66% said they are using or plan to use agentic AI in their operations, and 40% expect some degree of factory autonomy by 2030. At the same time, they’re wary of the potential risks: In Deloitte’s 2025 smart manufacturing survey (opens in new tab), 65% of executives ranked operational risk, including the potential for business disruption, among their top concerns.
Source: MIT Technology Review Insights with data from Manufacturing Leadership Council (opens in new tab), 2026